README.md

---
license: mit
task_categories:
- video-text-to-text
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---

# LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment

## Summary
This is the dataset proposed in our paper "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment". LiFT-HRA is a high-quality Human Preference Annotation dataset that can be used to train video-text-to-text reward models. All videos in the LiFT-HRA dataset have resolutions of at least 512×512.

Project: https://codegoat24.github.io/LiFT/

Code: https://github.com/CodeGoat24/LiFT

## Directory
```
DATA_PATH
└─ LiFT-HRA-data.json
└─ videos
    └─ HRA_part0.zip
    └─ HRA_part1.zip
    └─ HRA_part2.zip
```

## Usage
### Installation

1. Clone the github repository and navigate to LiFT folder
```bash
git clone https://github.com/CodeGoat24/LiFT.git
cd LiFT
```
2. Install packages
```
bash ./environment_setup.sh lift
```

### Training

**Dataset**

Please download this LiFT-HRA dataset and put it under `./dataset` directory. The data structure is like this:
```
dataset
├── LiFT-HRA
│  ├── LiFT-HRA-data.json
│  ├── videos
```

**Training**

LiFT-Critic-13b
```bash
bash LiFT_Critic/train/train_critic_13b.sh
```
LiFT-Critic-40b
```bash
bash LiFT_Critic/train/train_critic_40b.sh
```


## Model Weights
We provide pre-trained model weights LiFT-Critic on our LiFT-HRA dataset. Please refer to [here](https://huggingface.co/collections/Fudan-FUXI/lift-6756e628d83c390221e02857).


## Citation
If you find our dataset helpful, please cite our paper.

```bibtex
@article{LiFT,
  title={LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment.},
  author={Wang, Yibin and Tan, Zhiyu, and Wang, Junyan and Yang, Xiaomeng and Jin, Cheng and Li, Hao},
  journal={arXiv preprint arXiv:2412.04814},
  year={2024}
}
```